Current application status of multi-scale simulation and machine learning in research on high-entropy alloys
High-entropy alloys (HEAs) have garnered significant attention across various fields owing to their unique design incorporating multi-principal elements and remarkable comprehensive performance. Nevertheless, the enormous composition design space of HEAs makes conventional alloy design methods appea...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423017623 |
| _version_ | 1797646750032330752 |
|---|---|
| author | Deyu Jiang Lechun Xie Liqiang Wang |
| author_facet | Deyu Jiang Lechun Xie Liqiang Wang |
| author_sort | Deyu Jiang |
| collection | DOAJ |
| description | High-entropy alloys (HEAs) have garnered significant attention across various fields owing to their unique design incorporating multi-principal elements and remarkable comprehensive performance. Nevertheless, the enormous composition design space of HEAs makes conventional alloy design methods appear to be costly and inefficient. Recently, computer simulation technologies such as multi-scale simulation and machine learning have emerged as an efficient way to explore the composition design, structure, and performance simulation of HEAs.This review introduces the commonly used multi-scale simulation methods such as first-principles calculation, molecular dynamics simulation, Monte Carlo simulation, CALPHAD, finite element simulation, and machine learning. These methods not only simulate the microstructure and deformation behavior of HEAs but also predict crucial material properties like mechanical and physicochemical properties, thereby facilitating the design of HEAs. The current state-of-the-art advancements in multi-scale simulation and machine learning techniques for studying HEAs are summarized, encompassing their practical applications and potential limitations. The utilization of machine learning and multi-scale computation in materials science, as well as the future prospects are ultimately proposed. |
| first_indexed | 2024-03-11T15:07:15Z |
| format | Article |
| id | doaj.art-de0143f789d341168d9d4423155134f7 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-03-11T15:07:15Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-de0143f789d341168d9d4423155134f72023-10-30T06:02:52ZengElsevierJournal of Materials Research and Technology2238-78542023-09-012613411374Current application status of multi-scale simulation and machine learning in research on high-entropy alloysDeyu Jiang0Lechun Xie1Liqiang Wang2State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; National Facility for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, ChinaHubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China; Corresponding author. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China.State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; National Facility for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China; Corresponding author. State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240,China.High-entropy alloys (HEAs) have garnered significant attention across various fields owing to their unique design incorporating multi-principal elements and remarkable comprehensive performance. Nevertheless, the enormous composition design space of HEAs makes conventional alloy design methods appear to be costly and inefficient. Recently, computer simulation technologies such as multi-scale simulation and machine learning have emerged as an efficient way to explore the composition design, structure, and performance simulation of HEAs.This review introduces the commonly used multi-scale simulation methods such as first-principles calculation, molecular dynamics simulation, Monte Carlo simulation, CALPHAD, finite element simulation, and machine learning. These methods not only simulate the microstructure and deformation behavior of HEAs but also predict crucial material properties like mechanical and physicochemical properties, thereby facilitating the design of HEAs. The current state-of-the-art advancements in multi-scale simulation and machine learning techniques for studying HEAs are summarized, encompassing their practical applications and potential limitations. The utilization of machine learning and multi-scale computation in materials science, as well as the future prospects are ultimately proposed.http://www.sciencedirect.com/science/article/pii/S2238785423017623Multi-scale simulationMachine learningHigh-entropy alloysSimulation and predictionAlloy design |
| spellingShingle | Deyu Jiang Lechun Xie Liqiang Wang Current application status of multi-scale simulation and machine learning in research on high-entropy alloys Journal of Materials Research and Technology Multi-scale simulation Machine learning High-entropy alloys Simulation and prediction Alloy design |
| title | Current application status of multi-scale simulation and machine learning in research on high-entropy alloys |
| title_full | Current application status of multi-scale simulation and machine learning in research on high-entropy alloys |
| title_fullStr | Current application status of multi-scale simulation and machine learning in research on high-entropy alloys |
| title_full_unstemmed | Current application status of multi-scale simulation and machine learning in research on high-entropy alloys |
| title_short | Current application status of multi-scale simulation and machine learning in research on high-entropy alloys |
| title_sort | current application status of multi scale simulation and machine learning in research on high entropy alloys |
| topic | Multi-scale simulation Machine learning High-entropy alloys Simulation and prediction Alloy design |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423017623 |
| work_keys_str_mv | AT deyujiang currentapplicationstatusofmultiscalesimulationandmachinelearninginresearchonhighentropyalloys AT lechunxie currentapplicationstatusofmultiscalesimulationandmachinelearninginresearchonhighentropyalloys AT liqiangwang currentapplicationstatusofmultiscalesimulationandmachinelearninginresearchonhighentropyalloys |